Enabling Individualized Recommendations and Dynamic Pricing of Value-Added Services through Willingness-to-Pay Data

Backhaus Klaus, Becker Jörg, Beverungen Daniel, Frohs Margarethe, Müller Oliver, Weddeling Matthias, Knackstedt Ralf, Steiner Michael


Abstract
When managing their growing service portfolio, many manufacturers in B2B markets face two significant problems: They fail to communicate the value of their service offerings and they lack the capability to generate profits with value-added services. To tackle these two issues, we have built and evaluated a collaborative filtering recommender system which (a) makes individualized recommendations of potentially interesting value-added services when customers express interest in a particular physical product and also (b) leverages estimations of a customer's willingness to pay to allow for a dynamic pricing of those services and the incorporation of profitability considerations into the recommendation process. The recommender system is based on an adapted conjoint analysis method combined with a stepwise componential segmentation algorithm to collect individualized preference and willingness-to-pay data. Compared to other state-of-the-art approaches, our system requires significantly less customer input before making a recommendation, does not suffer from the usual sparseness of data and cold-start problems of collaborative filtering systems, and, as is shown in an empirical evaluation with a sample of 428 customers in the machine tool market, does not diminish the predictive accuracy of the recommendations offered.

Keywords
Collaborative filtering, Dynamic pricing, Willingness-to-pay, Service science, Design science



Publication type
Article in Journal

Peer reviewed
Yes

Publication status
Published

Year
2010

Journal
Electronic Markets

Volume
20

Issue
2

Start page
131

End page
146

Pages range
131-146

Language
English

DOI